Distributed and Cooperative Exploration for Multiple Targets Using Detection Maps

In this study, we propose a distributed search method for multiple targets in a two-dimensional space using detection maps. The proposed search method has the advantages of being robust to global area coverage, having a small computational load, and being fault-tolerant due to its distributed control. Specifically, each search agent shares its target detection history from its own search as a detection map with other agents, and is controlled according to the gradient of the potential field created based on this map, which enables distributed and efficient search activities. The contribution of this paper is that the proposed method improves the flexibility of conventional search methods by distributing them. Finally, simulation validation confirms the effectiveness of the proposed method.

Authors

Institutions

Publication Details

Journal
Transactions of the Society of Instrument and Control Engineers
Published
2025-01-01
DOI
https://doi.org/10.9746/sicetr.61.203
Primary Topic
Optimization and Search Problems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Distributed and Cooperative Exploration for Multiple Targets Using Detection Maps

Toru Namerikawa, Hiroyuki Kobayashi
Transactions of the Society of Instrument and Control Engineers
Optimization and Search Problems
article

Distributed and Cooperative Exploration for Multiple Targets Using Detection Maps

Toru Namerikawa, Hiroyuki Kobayashi
article en

Abstract

In this study, we propose a distributed search method for multiple targets in a two-dimensional space using detection maps. The proposed search method has the advantages of being robust to global area coverage, having a small computational load, and being fault-tolerant due to its distributed control. Specifically, each search agent shares its target detection history from its own search as a detection map with other agents, and is controlled according to the gradient of the potential field created based on this map, which enables distributed and efficient search activities. The contribution of this paper is that the proposed method improves the flexibility of conventional search methods by distributing them. Finally, simulation validation confirms the effectiveness of the proposed method.

Transactions of the Society of Instrument and Control EngineersVol. 61(3)
Keio University (JP)
Openalex Percentile: Top 95%
Optimization and Search Problems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.